scholarly journals Multiparametric MR Imaging for Detection of Clinically Significant Prostate Cancer: A Validation Cohort Study with Transperineal Template Prostate Mapping as the Reference Standard

Radiology ◽  
2013 ◽  
Vol 268 (3) ◽  
pp. 761-769 ◽  
Author(s):  
Nimalan Arumainayagam ◽  
Hashim U. Ahmed ◽  
Caroline M. Moore ◽  
Alex Freeman ◽  
Clare Allen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cohort Study ◽  
Mr Imaging ◽  
Validation Cohort ◽  
Reference Standard ◽  
Clinically Significant

scholarly journals Detection of Clinically Significant Prostate Cancer: Short Dual–Pulse Sequence versus Standard Multiparametric MR Imaging—A Multireader Study

Radiology ◽  
2017 ◽  
Vol 284 (3) ◽  
pp. 725-736 ◽  
Author(s):  
Borna K. Barth ◽  
Pieter J. L. De Visschere ◽  
Alexander Cornelius ◽  
Carlos Nicolau ◽  
Hebert Alberto Vargas ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mr Imaging ◽  
Pulse Sequence ◽  
Sequence Versus ◽  
Clinically Significant

scholarly journals Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer

2020 ◽  
Author(s):  
Constantinos Zamboglou ◽  
Alisa S. Bettermann ◽  
Christian Gratzke ◽  
Michael Mix ◽  
Juri Ruf ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Image Interpretation ◽  
Receiver Operating Curve ◽  
Training Cohort ◽  
Primary Prostate Cancer ◽  
Psma Pet ◽  
Number Of Patients ◽  
Clinically Significant

Abstract Introduction Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. Methodology This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. Results In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. Conclusion Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

scholarly journals Risk of dementia and Parkinson’s disease in patients treated with androgen deprivation therapy using gonadotropin-releasing hormone agonist for prostate cancer: A nationwide population-based cohort study

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244660
Author(s):  
Myungsun Shim ◽  
Woo Jin Bang ◽  
Cheol Young Oh ◽  
Yong Seong Lee ◽  
Seong Soo Jeon ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cohort Study ◽  
Propensity Score ◽  
Androgen Deprivation Therapy ◽  
Validation Cohort ◽  
Population Based ◽  
Gonadotropin Releasing Hormone ◽  
Gonadotropin Releasing Hormone Agonist

Recent studies reported conflicting results on the association of androgen deprivation therapy (ADT) with dementia and Parkinson’s disease in patients with prostate cancer (Pca). Therefore, this study aimed to investigate whether use of gonadotropin-releasing hormone agonist (GnRHa) increases the risk of both diseases. A nationwide population cohort study was conducted involving newly diagnosed patients with Pca %who started ADT with GnRHa (GnRHa users, n = 3,201) and control (nonusers, n = 4,123) between January 1, 2012, and December 31, 2016, using data from the National Health Insurance Service. To validate the result, a hospital cohort of patients with Pca consisting of GnRHa users (n = 205) and nonusers (n = 479) in a tertiary referral center from January 1, 2006 to December 31, 2016, were also analyzed. Traditional and propensity score-matched Cox proportional hazards models were used to estimate the effects of ADT on the risk of dementia and Parkinson’s disease. In univariable analysis, risk of dementia was associated with GnRHa use in both nationwide and hospital validation cohort (hazard ratio [HR], 1.696; 95% CI, 1.425–2.019, and HR, 1.352; 95% CI, 1.089–1.987, respectively). In a nationwide cohort, ADT was not associated with dementia in both traditional and propensity score-matched multivariable analysis, whereas in a hospital validation cohort, ADT was associated with dementia only in unmatched analysis (HR, 1.203; 95% CI, 1.021–1.859) but not in propensity score-matched analysis. ADT was not associated with Parkinson’s disease in either nationwide and validation cohorts. This population-based study suggests that the association between GnRHa use as ADT and increased risk of dementia or Parkinson’s disease is not clear, which was also verified in a hospital validation cohort.

scholarly journals Accuracy of fractal analysis and PI-RADS assessment of prostate magnetic resonance imaging for prediction of cancer grade groups: a clinical validation study

2021 ◽  
Author(s):  
Florian Michallek ◽  
Henkjan Huisman ◽  
Bernd Hamm ◽  
Sefer Elezkurtaj ◽  
Andreas Maxeiner ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Fractal Dimension ◽  
Diagnostic Accuracy ◽  
Fractal Analysis ◽  
High Sensitivity ◽  
Reference Standard ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Grade Groups ◽  
Clinically Significant

Abstract Objectives Multiparametric MRI with Prostate Imaging Reporting and Data System (PI-RADS) assessment is sensitive but not specific for detecting clinically significant prostate cancer. This study validates the diagnostic accuracy of the recently suggested fractal dimension (FD) of perfusion for detecting clinically significant cancer. Materials and methods Routine clinical MR imaging data, acquired at 3 T without an endorectal coil including dynamic contrast-enhanced sequences, of 72 prostate cancer foci in 64 patients were analyzed. In-bore MRI-guided biopsy with International Society of Urological Pathology (ISUP) grading served as reference standard. Previously established FD cutoffs for predicting tumor grade were compared to measurements of the apparent diffusion coefficient (25th percentile, ADC25) and PI-RADS assessment with and without inclusion of the FD as separate criterion. Results Fractal analysis allowed prediction of ISUP grade groups 1 to 4 but not 5, with high agreement to the reference standard (κFD = 0.88 [CI: 0.79–0.98]). Integrating fractal analysis into PI-RADS allowed a strong improvement in specificity and overall accuracy while maintaining high sensitivity for significant cancer detection (ISUP > 1; PI-RADS alone: sensitivity = 96%, specificity = 20%, area under the receiver operating curve [AUC] = 0.65; versus PI-RADS with fractal analysis: sensitivity = 95%, specificity = 88%, AUC = 0.92, p < 0.001). ADC25 only differentiated low-grade group 1 from pooled higher-grade groups 2–5 (κADC = 0.36 [CI: 0.12–0.59]). Importantly, fractal analysis was significantly more reliable than ADC25 in predicting non-significant and clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75, p < 0.001). Diagnostic accuracy was not significantly affected by zone location. Conclusions Fractal analysis is accurate in noninvasively predicting tumor grades in prostate cancer and adds independent information when implemented into PI-RADS assessment. This opens the opportunity to individually adjust biopsy priority and method in individual patients. Key Points • Fractal analysis of perfusion is accurate in noninvasively predicting tumor grades in prostate cancer using dynamic contrast-enhanced sequences (κFD = 0.88). • Including the fractal dimension into PI-RADS as a separate criterion improved specificity (from 20 to 88%) and overall accuracy (AUC from 0.86 to 0.96) while maintaining high sensitivity (96% versus 95%) for predicting clinically significant cancer. • Fractal analysis was significantly more reliable than ADC25 in predicting clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75).

Efficacy of 3T Multiparametric MR Imaging followed by 3T in-Bore MR-Guided Biopsy for Detection of Clinically Significant Prostate Cancer Based on PIRADSv2.1 Score

2020 ◽  
Vol 31 (10) ◽  
pp. 1619-1626
Author(s):  
Melina Hosseiny ◽  
Ely R. Felker ◽  
Afshin Azadikhah ◽  
Voraparee Suvannarerg ◽  
James Sayre ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mr Imaging ◽  
Guided Biopsy ◽  
Clinically Significant

scholarly journals A PI-RADS-Based New Nomogram for Predicting Clinically Significant Prostate Cancer: A Cohort Study

2020 ◽  
Vol Volume 12 ◽  
pp. 3631-3641 ◽  
Author(s):  
Yueyue Zhang ◽  
Guiqi Zhu ◽  
Wenlu Zhao ◽  
Chaogang Wei ◽  
Tong Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cohort Study ◽  
Clinically Significant
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